Prostate cancer is the most frequently diagnosed male cancer and the fifth leading cause of cancer-associated mortality in Western countries (1). Prostate cancer is typically diagnosed on the basis of increased serum prostate specific antigen (PSA) levels followed by histopathological inspection of needle biopsies.
The use of PSA for prostate cancer detection, however, is associated with considerable false positive rates and does not distinguish well between indolent and aggressive tumors. During the past decades, increased use of PSA testing and PSA based screening has resulted in higher incidences as well as down-staging of the disease.
However, PSA as well as the other currently available prognostic indicators (mainly number of positive biopsies, clinical TNM stage and Gleason score) are unable to accurately predict patients with an aggressive prostate cancer that requires instant treatment. This leads to marked overtreatment, and many patients undergo unnecessary RP or radiation therapy, which is associated with side effects worse than living with the untreated non-lethal prostate cancer.
Hence, there is a serious unmet need in prostate cancer diagnostics to develop methods which can improve the prognostic assessment by correctly distinguishing between non-aggressive cancers, that safely can be managed by active surveillance, and aggressive cancers that will benefit from early intervention.
An emerging new class of potential biomarkers for prostate cancer is the microRNA.
MicroRNAs comprise a class of endogenous small non-coding regulatory RNAs (˜22 nt), which control gene expression at the posttranscriptional level in diverse organisms, including mammals (2). MicroRNAs are transcribed as long imperfect paired stem-loop primary microRNA transcripts (pri-microRNAs) by RNA polymerase II, and further processed into hairpin precursor microRNAs (pre-microRNAs) by the nuclear RNase III endonuclease, Drosha (3). After export to the cytoplasm by Exportin-5-Ran-GTP, another RNase III endonuclease, Dicer, cleaves the pre-microRNA into a mature ˜22 nt microRNA duplex (3). Mature microRNAs mediate their function while incorporated in the microRNA-induced silencing complex (miRISC). The microRNA guides this complex to perfect/near perfect complementary target mRNAs, leading to either translational inhibition or mRNA degradation (4).
MicroRNAs are one of the most abundant classes of gene regulatory molecules and the latest release of the miRBase (version 21) contains 2588 mature human microRNAs (1881 precursors) http://www.mirbase.org/ (5). Together microRNAs have been estimated to regulate up to two thirds of all human mRNAs. Consequently, microRNAs influence numerous processes in the cell, for instance cell differentiation, cell cycle progression and apoptosis, and deregulation of microRNAs are often connected to human pathologies, including cancer (6). Additionally, some microRNAs appear to be cell type and disease specific and deregulated microRNA expression has been associated with both development and progression of cancer (7). Thus, aberrant microRNA expression has been investigated as a promising potential source of novel biomarkers for early cancer diagnosis (7). Moreover, microRNAs have potential to be used as targets of microRNA-based therapeutics for cancer (8). Several microRNA profiling studies have also reported aberrantly expressed microRNAs in the development and/or progression of prostate cancer (9). However, most of the microRNA biomarker studies in prostate cancer published to date have used relatively low patient sample numbers and often lack stringent independent clinical validation to confirm the biomarker potential of the identified microRNA candidates.
Importantly, to the best of our knowledge, no prognostic method based on microRNA biomarkers able to predict the risk of prostate cancer recurrence has been discovered.
Here we performed miRnome profiling of more than 750 of the most abundant microRNAs and identified the significantly aberrant regulated microRNAs in prostate tumor tissue FFPE samples from patients with vs. without biochemical recurrence (BCR) after radical prostatectomy (RP). We identified five prognostic classifiers in cohort 1 and evaluated their prognostic accuracy as predictors of time to recurrence—monitored as biochemical recurrence (PSA) after removal of the prostate (radical prostatectomy (RP)) (Example 1). The prognostic accuracy of the classifiers was then validated in two independent radical prostatectomy cohorts (cohort 2 and cohort 3) (Example 2-6). Despite the fact that prostate tumor samples in cohort 3 were of different national origin (U.S.), sampled in a different manner (snap-frozen), subjected to different RNA extraction procedures, analyzed by a different microRNA expression detection platform, and different Cohort characteristics (Cohort 3 was generally less aggressive and had fewer events of recurrence than cohort 1 and 2), four of our microRNA prognostic classifier performed equally well on the external cohort, underlining the robustness of these classifiers.
The five prognostic microRNA classifiers all showed significant independent prognostic value for prediction of time to BCR after RP, beyond routine clinicopathological variables.